Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations2371
Missing cells9981
Missing cells (%)28.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory903.1 KiB
Average record size in memory390.0 B

Variable types

Categorical4
DateTime2
Numeric8
Text1

Alerts

Air Temp Celsius is highly overall correlated with Air Temp Fahrenheit and 4 other fieldsHigh correlation
Air Temp Fahrenheit is highly overall correlated with Air Temp Celsius and 2 other fieldsHigh correlation
Dissolved Oxygen milligram per lit is highly overall correlated with UnitId and 1 other fieldsHigh correlation
Field Technician is highly overall correlated with WhoVerifiedHigh correlation
Salinity in ppt is highly overall correlated with UnitIdHigh correlation
Secchi Depth meters is highly overall correlated with Water Depth metersHigh correlation
UnitId is highly overall correlated with Air Temp Celsius and 4 other fieldsHigh correlation
Water Depth meters is highly overall correlated with Secchi Depth metersHigh correlation
Water Temp Celsius is highly overall correlated with Air Temp Celsius and 2 other fieldsHigh correlation
WhoVerified is highly overall correlated with Air Temp Celsius and 2 other fieldsHigh correlation
pH is highly overall correlated with Air Temp CelsiusHigh correlation
UnitId has 2339 (98.7%) missing values Missing
Salinity in ppt has 130 (5.5%) missing values Missing
Dissolved Oxygen milligram per lit has 851 (35.9%) missing values Missing
pH has 95 (4.0%) missing values Missing
Secchi Depth meters has 73 (3.1%) missing values Missing
Water Depth meters has 71 (3.0%) missing values Missing
Water Temp Celsius has 121 (5.1%) missing values Missing
Air Temp Celsius has 2286 (96.4%) missing values Missing
Air Temp Fahrenheit has 71 (3.0%) missing values Missing
Time 24 clcok has 63 (2.7%) missing values Missing
Field Technician has 39 (1.6%) missing values Missing
DateVerified has 1918 (80.9%) missing values Missing
WhoVerified has 1918 (80.9%) missing values Missing
Salinity in ppt has 1440 (60.7%) zeros Zeros

Reproduction

Analysis started2025-02-22 18:22:28.499117
Analysis finished2025-02-22 18:22:59.776558
Duration31.28 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

SiteId
Categorical

Distinct6
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Memory size117.5 KiB
Bay
794 
D
440 
B
437 
A
434 
C
264 

Length

Max length3
Median length1
Mean length1.6700422
Min length1

Characters and Unicode

Total characters3958
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBay
2nd rowBay
3rd rowBay
4th rowBay
5th rowBay

Common Values

ValueCountFrequency (%)
Bay 794
33.5%
D 440
18.6%
B 437
18.4%
A 434
18.3%
C 264
 
11.1%
d 1
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2025-02-22T13:23:00.187980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T13:23:00.654290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
bay 794
33.5%
d 441
18.6%
b 437
18.4%
a 434
18.3%
c 264
 
11.1%

Most occurring characters

ValueCountFrequency (%)
B 1231
31.1%
a 794
20.1%
y 794
20.1%
D 440
 
11.1%
A 434
 
11.0%
C 264
 
6.7%
d 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3958
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 1231
31.1%
a 794
20.1%
y 794
20.1%
D 440
 
11.1%
A 434
 
11.0%
C 264
 
6.7%
d 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3958
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 1231
31.1%
a 794
20.1%
y 794
20.1%
D 440
 
11.1%
A 434
 
11.0%
C 264
 
6.7%
d 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3958
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 1231
31.1%
a 794
20.1%
y 794
20.1%
D 440
 
11.1%
A 434
 
11.0%
C 264
 
6.7%
d 1
 
< 0.1%

UnitId
Categorical

High correlation  Missing 

Distinct2
Distinct (%)6.2%
Missing2339
Missing (%)98.7%
Memory size129.7 KiB
01csv
28 
01CSV

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters160
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01CSV
2nd row01CSV
3rd row01CSV
4th row01CSV
5th row01csv

Common Values

ValueCountFrequency (%)
01csv 28
 
1.2%
01CSV 4
 
0.2%
(Missing) 2339
98.7%

Length

2025-02-22T13:23:01.107824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T13:23:01.465334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
01csv 32
100.0%

Most occurring characters

ValueCountFrequency (%)
0 32
20.0%
1 32
20.0%
c 28
17.5%
s 28
17.5%
v 28
17.5%
C 4
 
2.5%
S 4
 
2.5%
V 4
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32
20.0%
1 32
20.0%
c 28
17.5%
s 28
17.5%
v 28
17.5%
C 4
 
2.5%
S 4
 
2.5%
V 4
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32
20.0%
1 32
20.0%
c 28
17.5%
s 28
17.5%
v 28
17.5%
C 4
 
2.5%
S 4
 
2.5%
V 4
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32
20.0%
1 32
20.0%
c 28
17.5%
s 28
17.5%
v 28
17.5%
C 4
 
2.5%
S 4
 
2.5%
V 4
 
2.5%
Distinct801
Distinct (%)33.9%
Missing5
Missing (%)0.2%
Memory size18.7 KiB
Minimum1989-05-11 00:00:00
Maximum2019-11-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-22T13:23:01.894142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:23:02.452347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Salinity in ppt
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct61
Distinct (%)2.7%
Missing130
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean0.71706827
Minimum0
Maximum9
Zeros1440
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size18.7 KiB
2025-02-22T13:23:02.976684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3.5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2308187
Coefficient of variation (CV)1.7164596
Kurtosis4.2688938
Mean0.71706827
Median Absolute Deviation (MAD)0
Skewness1.9728464
Sum1606.95
Variance1.5149147
MonotonicityNot monotonic
2025-02-22T13:23:03.477181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1440
60.7%
1 245
 
10.3%
3 92
 
3.9%
2 84
 
3.5%
1.5 39
 
1.6%
4 24
 
1.0%
2.5 20
 
0.8%
0.1 17
 
0.7%
3.5 16
 
0.7%
1.2 15
 
0.6%
Other values (51) 249
 
10.5%
(Missing) 130
 
5.5%
ValueCountFrequency (%)
0 1440
60.7%
0.01 2
 
0.1%
0.05 1
 
< 0.1%
0.08 2
 
0.1%
0.09 1
 
< 0.1%
0.1 17
 
0.7%
0.18 1
 
< 0.1%
0.2 5
 
0.2%
0.24 1
 
< 0.1%
0.3 1
 
< 0.1%
ValueCountFrequency (%)
9 2
 
0.1%
8 2
 
0.1%
6.2 1
 
< 0.1%
5.5 1
 
< 0.1%
5.4 1
 
< 0.1%
5.1 3
 
0.1%
5 3
 
0.1%
4.8 3
 
0.1%
4.7 6
0.3%
4.5 11
0.5%

Dissolved Oxygen milligram per lit
Real number (ℝ)

High correlation  Missing 

Distinct154
Distinct (%)10.1%
Missing851
Missing (%)35.9%
Infinite0
Infinite (%)0.0%
Mean6.6462632
Minimum0
Maximum15.1
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size18.7 KiB
2025-02-22T13:23:03.968711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.9
Q14.8
median6.5
Q38.5
95-th percentile10.8
Maximum15.1
Range15.1
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation2.5066075
Coefficient of variation (CV)0.37714539
Kurtosis-0.40814292
Mean6.6462632
Median Absolute Deviation (MAD)1.8
Skewness0.18522675
Sum10102.32
Variance6.2830814
MonotonicityNot monotonic
2025-02-22T13:23:04.674930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 47
 
2.0%
5 35
 
1.5%
6.3 33
 
1.4%
6 32
 
1.3%
4.2 31
 
1.3%
5.9 30
 
1.3%
5.3 30
 
1.3%
5.2 28
 
1.2%
8 27
 
1.1%
6.9 27
 
1.1%
Other values (144) 1200
50.6%
(Missing) 851
35.9%
ValueCountFrequency (%)
0 4
0.2%
0.1 1
 
< 0.1%
0.8 3
0.1%
1.1 1
 
< 0.1%
1.2 1
 
< 0.1%
1.35 1
 
< 0.1%
1.5 3
0.1%
1.6 2
0.1%
1.7 1
 
< 0.1%
1.8 3
0.1%
ValueCountFrequency (%)
15.1 1
< 0.1%
14.3 1
< 0.1%
14.2 1
< 0.1%
13.8 1
< 0.1%
13.3 1
< 0.1%
13.2 2
0.1%
13 1
< 0.1%
12.9 1
< 0.1%
12.8 1
< 0.1%
12.7 1
< 0.1%

pH
Real number (ℝ)

High correlation  Missing 

Distinct48
Distinct (%)2.1%
Missing95
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean7.1682118
Minimum0.3
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.7 KiB
2025-02-22T13:23:05.495473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile6.5
Q16.5
median7
Q37.5
95-th percentile8.7
Maximum9.9
Range9.6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78848516
Coefficient of variation (CV)0.10999747
Kurtosis4.5160324
Mean7.1682118
Median Absolute Deviation (MAD)0.5
Skewness0.26965677
Sum16314.85
Variance0.62170884
MonotonicityNot monotonic
2025-02-22T13:23:06.028601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
7 660
27.8%
6.5 654
27.6%
7.5 242
 
10.2%
8 144
 
6.1%
6 81
 
3.4%
8.5 71
 
3.0%
9 38
 
1.6%
8.1 26
 
1.1%
8.2 26
 
1.1%
7.9 24
 
1.0%
Other values (38) 310
13.1%
(Missing) 95
 
4.0%
ValueCountFrequency (%)
0.3 1
 
< 0.1%
0.7 1
 
< 0.1%
4.8 1
 
< 0.1%
5 3
 
0.1%
5.5 3
 
0.1%
5.6 1
 
< 0.1%
5.7 1
 
< 0.1%
6 81
3.4%
6.25 1
 
< 0.1%
6.3 4
 
0.2%
ValueCountFrequency (%)
9.9 2
 
0.1%
9.8 1
 
< 0.1%
9.7 1
 
< 0.1%
9.6 1
 
< 0.1%
9.5 6
 
0.3%
9.4 2
 
0.1%
9.3 8
 
0.3%
9.2 12
 
0.5%
9.1 11
 
0.5%
9 38
1.6%

Secchi Depth meters
Real number (ℝ)

High correlation  Missing 

Distinct77
Distinct (%)3.4%
Missing73
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean0.52489774
Minimum0
Maximum9
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size18.7 KiB
2025-02-22T13:23:06.892629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.3
median0.4
Q30.65
95-th percentile1.2
Maximum9
Range9
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.47366339
Coefficient of variation (CV)0.90239176
Kurtosis88.984356
Mean0.52489774
Median Absolute Deviation (MAD)0.2
Skewness7.0604798
Sum1206.215
Variance0.22435701
MonotonicityNot monotonic
2025-02-22T13:23:07.421511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 283
11.9%
0.3 277
11.7%
0.5 266
11.2%
0.2 243
10.2%
0.6 166
 
7.0%
0.7 132
 
5.6%
0.1 116
 
4.9%
0.8 103
 
4.3%
0.9 83
 
3.5%
0.25 80
 
3.4%
Other values (67) 549
23.2%
ValueCountFrequency (%)
0 5
 
0.2%
0.01 3
 
0.1%
0.03 2
 
0.1%
0.05 9
 
0.4%
0.07 1
 
< 0.1%
0.1 116
4.9%
0.12 1
 
< 0.1%
0.15 40
 
1.7%
0.16 1
 
< 0.1%
0.17 2
 
0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
7.4 1
 
< 0.1%
5.8 1
 
< 0.1%
5.5 2
0.1%
4.5 2
0.1%
4 3
0.1%
3.5 1
 
< 0.1%
3 1
 
< 0.1%
2.5 4
0.2%
2.2 1
 
< 0.1%

Water Depth meters
Real number (ℝ)

High correlation  Missing 

Distinct114
Distinct (%)5.0%
Missing71
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean0.7625587
Minimum0.01
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.7 KiB
2025-02-22T13:23:07.931810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.2
Q10.4
median0.65
Q30.95
95-th percentile1.5
Maximum12
Range11.99
Interquartile range (IQR)0.55

Descriptive statistics

Standard deviation0.62114039
Coefficient of variation (CV)0.81454764
Kurtosis78.084693
Mean0.7625587
Median Absolute Deviation (MAD)0.25
Skewness6.1938396
Sum1753.885
Variance0.38581538
MonotonicityNot monotonic
2025-02-22T13:23:08.468119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 179
 
7.5%
0.6 172
 
7.3%
0.3 158
 
6.7%
0.4 154
 
6.5%
0.7 153
 
6.5%
0.8 148
 
6.2%
0.9 138
 
5.8%
0.2 133
 
5.6%
1.5 86
 
3.6%
1.4 81
 
3.4%
Other values (104) 898
37.9%
ValueCountFrequency (%)
0.01 3
 
0.1%
0.03 2
 
0.1%
0.05 5
 
0.2%
0.07 1
 
< 0.1%
0.1 72
3.0%
0.11 1
 
< 0.1%
0.12 1
 
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
0.15 24
 
1.0%
ValueCountFrequency (%)
12 1
 
< 0.1%
8.5 1
 
< 0.1%
7.5 1
 
< 0.1%
7.4 1
 
< 0.1%
7 1
 
< 0.1%
5.8 1
 
< 0.1%
5.5 1
 
< 0.1%
5 1
 
< 0.1%
4.5 3
0.1%
4 1
 
< 0.1%

Water Temp Celsius
Real number (ℝ)

High correlation  Missing 

Distinct114
Distinct (%)5.1%
Missing121
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean18.062138
Minimum0
Maximum74
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size18.7 KiB
2025-02-22T13:23:08.965733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median19
Q325
95-th percentile29
Maximum74
Range74
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.2982461
Coefficient of variation (CV)0.45942768
Kurtosis0.41431068
Mean18.062138
Median Absolute Deviation (MAD)7
Skewness0.13902201
Sum40639.81
Variance68.860888
MonotonicityNot monotonic
2025-02-22T13:23:09.500926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 127
 
5.4%
26 120
 
5.1%
28 117
 
4.9%
25 116
 
4.9%
20 88
 
3.7%
13 82
 
3.5%
12 82
 
3.5%
10 80
 
3.4%
14 79
 
3.3%
9 75
 
3.2%
Other values (104) 1284
54.2%
(Missing) 121
 
5.1%
ValueCountFrequency (%)
0 4
 
0.2%
0.7 1
 
< 0.1%
0.9 1
 
< 0.1%
1 6
 
0.3%
1.3 1
 
< 0.1%
1.6 1
 
< 0.1%
2 11
0.5%
2.5 2
 
0.1%
3 22
0.9%
3.5 1
 
< 0.1%
ValueCountFrequency (%)
74 1
 
< 0.1%
60 1
 
< 0.1%
59 1
 
< 0.1%
54 1
 
< 0.1%
48 1
 
< 0.1%
43 1
 
< 0.1%
42.5 1
 
< 0.1%
35 2
0.1%
34.5 2
0.1%
34 3
0.1%

Air Temp Celsius
Real number (ℝ)

High correlation  Missing 

Distinct45
Distinct (%)52.9%
Missing2286
Missing (%)96.4%
Infinite0
Infinite (%)0.0%
Mean16.437647
Minimum0
Maximum74
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size18.7 KiB
2025-02-22T13:23:10.072001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median15
Q321.7
95-th percentile28.4
Maximum74
Range74
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation11.754138
Coefficient of variation (CV)0.71507423
Kurtosis12.079605
Mean16.437647
Median Absolute Deviation (MAD)6
Skewness2.796289
Sum1397.2
Variance138.15976
MonotonicityNot monotonic
2025-02-22T13:23:10.770635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
9 7
 
0.3%
13 6
 
0.3%
19 5
 
0.2%
8 4
 
0.2%
7 4
 
0.2%
3 4
 
0.2%
16 4
 
0.2%
11 3
 
0.1%
10 3
 
0.1%
15 3
 
0.1%
Other values (35) 42
 
1.8%
(Missing) 2286
96.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
2.6 1
 
< 0.1%
2.7 1
 
< 0.1%
3 4
0.2%
5 1
 
< 0.1%
7 4
0.2%
7.6 1
 
< 0.1%
8 4
0.2%
9 7
0.3%
9.5 1
 
< 0.1%
ValueCountFrequency (%)
74 2
0.1%
37 1
< 0.1%
30.5 1
< 0.1%
28.5 1
< 0.1%
28 1
< 0.1%
27.9 1
< 0.1%
27 1
< 0.1%
26.9 1
< 0.1%
25.7 1
< 0.1%
25.5 1
< 0.1%

Air Temp Fahrenheit
Real number (ℝ)

High correlation  Missing 

Distinct225
Distinct (%)9.8%
Missing71
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean62.051637
Minimum10.5
Maximum92.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.7 KiB
2025-02-22T13:23:11.437353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile37
Q149
median63
Q375
95-th percentile83
Maximum92.3
Range81.8
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.492236
Coefficient of variation (CV)0.24966685
Kurtosis-0.73817032
Mean62.051637
Median Absolute Deviation (MAD)13
Skewness-0.35926572
Sum142718.76
Variance240.00939
MonotonicityNot monotonic
2025-02-22T13:23:12.206033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 85
 
3.6%
80 66
 
2.8%
70 66
 
2.8%
72 63
 
2.7%
58 59
 
2.5%
45 58
 
2.4%
60 58
 
2.4%
82 55
 
2.3%
78 55
 
2.3%
73 53
 
2.2%
Other values (215) 1682
70.9%
(Missing) 71
 
3.0%
ValueCountFrequency (%)
10.5 1
 
< 0.1%
11.5 1
 
< 0.1%
12 1
 
< 0.1%
14.5 1
 
< 0.1%
17 1
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
23 2
 
0.1%
25 4
 
0.2%
26 15
0.6%
ValueCountFrequency (%)
92.3 1
 
< 0.1%
91.4 1
 
< 0.1%
90.5 1
 
< 0.1%
89 2
 
0.1%
88.7 1
 
< 0.1%
88 5
 
0.2%
87.8 5
 
0.2%
87.4 1
 
< 0.1%
87.1 1
 
< 0.1%
87 14
0.6%

Time 24 clcok
Text

Missing 

Distinct90
Distinct (%)3.9%
Missing63
Missing (%)2.7%
Memory size117.5 KiB
2025-02-22T13:23:13.418380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length1
Mean length2.1889081
Min length1

Characters and Unicode

Total characters5052
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)1.5%

Sample

1st row11
2nd row11.3
3rd row9.45
4th row10.3
5th row10
ValueCountFrequency (%)
0 1440
62.4%
14.24 52
 
2.3%
19.12 47
 
2.0%
4.48 46
 
2.0%
9.36 44
 
1.9%
10.15 42
 
1.8%
10.45 39
 
1.7%
9.45 38
 
1.6%
21.36 37
 
1.6%
12 36
 
1.6%
Other values (80) 487
 
21.1%
2025-02-22T13:23:15.080017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1774
35.1%
1 848
16.8%
. 779
15.4%
4 374
 
7.4%
2 317
 
6.3%
5 294
 
5.8%
9 213
 
4.2%
3 210
 
4.2%
6 111
 
2.2%
8 88
 
1.7%
Other values (3) 44
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1774
35.1%
1 848
16.8%
. 779
15.4%
4 374
 
7.4%
2 317
 
6.3%
5 294
 
5.8%
9 213
 
4.2%
3 210
 
4.2%
6 111
 
2.2%
8 88
 
1.7%
Other values (3) 44
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1774
35.1%
1 848
16.8%
. 779
15.4%
4 374
 
7.4%
2 317
 
6.3%
5 294
 
5.8%
9 213
 
4.2%
3 210
 
4.2%
6 111
 
2.2%
8 88
 
1.7%
Other values (3) 44
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1774
35.1%
1 848
16.8%
. 779
15.4%
4 374
 
7.4%
2 317
 
6.3%
5 294
 
5.8%
9 213
 
4.2%
3 210
 
4.2%
6 111
 
2.2%
8 88
 
1.7%
Other values (3) 44
 
0.9%

Field Technician
Categorical

High correlation  Missing 

Distinct14
Distinct (%)0.6%
Missing39
Missing (%)1.6%
Memory size137.2 KiB
Not Recorded
1225 
S. Poe
358 
Sue Poe
342 
Feldman
172 
Susan Poe
 
86
Other values (9)
149 

Length

Max length24
Median length12
Mean length10.231132
Min length6

Characters and Unicode

Total characters23859
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowJohn Phillips
2nd rowJohn Phillips
3rd rowJohn Phillips
4th rowJohn Phillips
5th rowJohn Phillips

Common Values

ValueCountFrequency (%)
Not Recorded 1225
51.7%
S. Poe 358
 
15.1%
Sue Poe 342
 
14.4%
Feldman 172
 
7.3%
Susan Poe 86
 
3.6%
J Phillips, Mary Feldman 37
 
1.6%
Strader, Pease, Feldman 35
 
1.5%
Pease, Strader 31
 
1.3%
John Phillips 17
 
0.7%
Strader 15
 
0.6%
Other values (4) 14
 
0.6%
(Missing) 39
 
1.6%

Length

2025-02-22T13:23:15.605424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
not 1225
26.6%
recorded 1225
26.6%
poe 800
17.4%
s 369
 
8.0%
sue 345
 
7.5%
feldman 244
 
5.3%
strader 92
 
2.0%
susan 86
 
1.9%
pease 66
 
1.4%
phillips 54
 
1.2%
Other values (3) 91
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 4062
17.0%
o 3266
13.7%
d 2786
11.7%
2265
9.5%
r 1446
 
6.1%
t 1317
 
5.5%
N 1225
 
5.1%
R 1225
 
5.1%
c 1225
 
5.1%
P 919
 
3.9%
Other values (18) 4123
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23859
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4062
17.0%
o 3266
13.7%
d 2786
11.7%
2265
9.5%
r 1446
 
6.1%
t 1317
 
5.5%
N 1225
 
5.1%
R 1225
 
5.1%
c 1225
 
5.1%
P 919
 
3.9%
Other values (18) 4123
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23859
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4062
17.0%
o 3266
13.7%
d 2786
11.7%
2265
9.5%
r 1446
 
6.1%
t 1317
 
5.5%
N 1225
 
5.1%
R 1225
 
5.1%
c 1225
 
5.1%
P 919
 
3.9%
Other values (18) 4123
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23859
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4062
17.0%
o 3266
13.7%
d 2786
11.7%
2265
9.5%
r 1446
 
6.1%
t 1317
 
5.5%
N 1225
 
5.1%
R 1225
 
5.1%
c 1225
 
5.1%
P 919
 
3.9%
Other values (18) 4123
17.3%

DateVerified
Date

Missing 

Distinct44
Distinct (%)9.7%
Missing1918
Missing (%)80.9%
Memory size18.7 KiB
Minimum2014-03-27 00:00:00
Maximum2019-11-13 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-22T13:23:16.083318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:23:16.652356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)

WhoVerified
Categorical

High correlation  Missing 

Distinct12
Distinct (%)2.6%
Missing1918
Missing (%)80.9%
Memory size132.8 KiB
Christine Folks
171 
Kayla Braasch
62 
Carly Sibilia
53 
Rebecca Walawender
51 
Erin Bailey
40 
Other values (7)
76 

Length

Max length18
Median length15
Mean length13.874172
Min length8

Characters and Unicode

Total characters6285
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowKaren Beatty
2nd rowKaren Beatty
3rd rowKaren Beatty
4th rowKaren Beatty
5th rowKaren Beatty

Common Values

ValueCountFrequency (%)
Christine Folks 171
 
7.2%
Kayla Braasch 62
 
2.6%
Carly Sibilia 53
 
2.2%
Rebecca Walawender 51
 
2.2%
Erin Bailey 40
 
1.7%
Karen Beatty 28
 
1.2%
Amy Keiler 23
 
1.0%
Karen Callaway 12
 
0.5%
K Beatty 5
 
0.2%
Trenton Miller 4
 
0.2%
Other values (2) 4
 
0.2%
(Missing) 1918
80.9%

Length

2025-02-22T13:23:17.197804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
christine 171
18.9%
folks 171
18.9%
kayla 62
 
6.8%
braasch 62
 
6.8%
carly 53
 
5.8%
sibilia 53
 
5.8%
rebecca 51
 
5.6%
walawender 51
 
5.6%
karen 40
 
4.4%
bailey 40
 
4.4%
Other values (8) 152
16.8%

Most occurring characters

ValueCountFrequency (%)
a 660
 
10.5%
i 609
 
9.7%
e 547
 
8.7%
l 486
 
7.7%
453
 
7.2%
r 449
 
7.1%
s 404
 
6.4%
n 310
 
4.9%
t 247
 
3.9%
C 236
 
3.8%
Other values (20) 1884
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6285
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 660
 
10.5%
i 609
 
9.7%
e 547
 
8.7%
l 486
 
7.7%
453
 
7.2%
r 449
 
7.1%
s 404
 
6.4%
n 310
 
4.9%
t 247
 
3.9%
C 236
 
3.8%
Other values (20) 1884
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6285
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 660
 
10.5%
i 609
 
9.7%
e 547
 
8.7%
l 486
 
7.7%
453
 
7.2%
r 449
 
7.1%
s 404
 
6.4%
n 310
 
4.9%
t 247
 
3.9%
C 236
 
3.8%
Other values (20) 1884
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6285
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 660
 
10.5%
i 609
 
9.7%
e 547
 
8.7%
l 486
 
7.7%
453
 
7.2%
r 449
 
7.1%
s 404
 
6.4%
n 310
 
4.9%
t 247
 
3.9%
C 236
 
3.8%
Other values (20) 1884
30.0%

Interactions

2025-02-22T13:22:53.957630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:30.913868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:34.501275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:37.645268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:41.046934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:44.901482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:47.900494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:51.252712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:54.345762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:31.362838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:34.914507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:37.968208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:41.469923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:45.348250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:48.250941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:51.607771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:54.691966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:31.795650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:35.359387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:38.598120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:42.010846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:45.724290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:48.999326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:51.942864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:55.053476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:32.168448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:35.729530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:38.909986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:42.468076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:46.078000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:49.363241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:52.278974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:55.404205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:32.549162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:36.155335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:39.251122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:42.875256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:46.420412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:49.716563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:52.629836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:55.808849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:32.925832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:36.505592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:39.639989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:43.423543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:46.792142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:50.106827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:52.983027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:56.204642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:33.384251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:36.888354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:40.084499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:43.842385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:47.163559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:50.506032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:53.293365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:56.524435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:33.952135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:37.238941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:40.556940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:44.372779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:47.511741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:50.852806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-22T13:22:53.628168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-02-22T13:23:17.546249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Air Temp CelsiusAir Temp FahrenheitDissolved Oxygen milligram per litField TechnicianSalinity in pptSecchi Depth metersSiteIdUnitIdWater Depth metersWater Temp CelsiusWhoVerifiedpH
Air Temp Celsius1.0000.860-0.0760.0000.342-0.0610.3421.0000.2300.9070.8930.579
Air Temp Fahrenheit0.8601.000-0.4470.0580.022-0.0360.0000.7390.0220.9140.3220.226
Dissolved Oxygen milligram per lit-0.076-0.4471.0000.2360.348-0.1420.2060.5400.024-0.5230.2640.080
Field Technician0.0000.0580.2361.0000.2860.0760.4020.0000.0840.0000.7070.169
Salinity in ppt0.3420.0220.3480.2861.000-0.2300.1961.000-0.089-0.0570.0000.382
Secchi Depth meters-0.061-0.036-0.1420.076-0.2301.0000.2220.0000.750-0.0100.000-0.272
SiteId0.3420.0000.2060.4020.1960.2221.0000.2960.3420.0540.2040.157
UnitId1.0000.7390.5400.0001.0000.0000.2961.0000.0000.0000.9810.000
Water Depth meters0.2300.0220.0240.084-0.0890.7500.3420.0001.0000.0220.000-0.254
Water Temp Celsius0.9070.914-0.5230.000-0.057-0.0100.0540.0000.0221.0000.3280.190
WhoVerified0.8930.3220.2640.7070.0000.0000.2040.9810.0000.3281.0000.137
pH0.5790.2260.0800.1690.382-0.2720.1570.000-0.2540.1900.1371.000

Missing values

2025-02-22T13:22:57.119651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-22T13:22:58.016862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-22T13:22:58.985986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SiteIdUnitIdReadDateSalinity in pptDissolved Oxygen milligram per litpHSecchi Depth metersWater Depth metersWater Temp CelsiusAir Temp CelsiusAir Temp FahrenheitTime 24 clcokField TechnicianDateVerifiedWhoVerified
0BayNaN01-03-19941.311.77.30.400.405.98.046.4011NaNNaNNaN
1BayNaN1/31/19941.512.07.40.200.353.02.636.6811.3NaNNaNNaN
2BayNaN02-07-19941.010.57.20.250.605.97.645.689.45NaNNaNNaN
3BayNaN2/23/19941.010.17.40.350.5010.02.736.86NaNNaNNaNNaN
4BayNaN2/28/19941.012.67.20.200.401.60.032.0010.3NaNNaNNaN
5BayNaN03-07-19941.09.97.10.200.909.715.259.3610NaNNaNNaN
6BayNaN3/14/19940.510.47.20.250.759.810.150.1811NaNNaNNaN
7BayNaN3/28/19941.09.27.10.150.9516.122.171.789.5NaNNaNNaN
8BayNaN04-04-19941.09.27.20.250.7515.013.556.3010NaNNaNNaN
9BayNaN04-11-19941.08.67.30.200.7515.713.055.4010NaNNaNNaN
SiteIdUnitIdReadDateSalinity in pptDissolved Oxygen milligram per litpHSecchi Depth metersWater Depth metersWater Temp CelsiusAir Temp CelsiusAir Temp FahrenheitTime 24 clcokField TechnicianDateVerifiedWhoVerified
2361DNaN10-11-20180.06.06.50.701.226.0NaN78.010.1Sue Poe11-12-2019Christine Folks
2362DNaN10/24/20180.0NaN6.50.601.316.0NaN58.010.15Sue Poe11-12-2019Christine Folks
2363DNaN10/28/20180.0NaN6.51.101.213.0NaN49.010.15Sue Poe11-12-2019Christine Folks
2364DNaN11-07-20180.06.96.50.901.320.0NaN65.010.22Sue Poe11-12-2019Christine Folks
2365DNaN12-11-20180.0NaN6.51.201.411.0NaN42.010.15Sue Poe11-12-2019Christine Folks
2366BayNaN10-11-20181.95.07.04.001.225.0NaN78.09.3Sue Poe11/13/2019Christine Folks
2367BayNaN10/24/20180.09.07.00.300.618.0NaN58.09.3Sue Poe11/13/2019Christine Folks
2368BayNaN10/28/20180.92.97.00.400.913.0NaN49.09.2Sue Poe11/13/2019Christine Folks
2369BayNaN11-07-20181.7NaN7.00.450.920.0NaN65.09.45Sue Poe11/13/2019Christine Folks
2370BayNaN12-11-20180.1NaN7.00.100.110.0NaN42.09.4Sue Poe11/13/2019Christine Folks